Over the past months, developers have moved from formulating detailed prompts to engineering the context that agentic coding tools read and to configuring the underlying harnesses. For example, developers commit context files, skills, hooks, and MCP configurations to their repositories, and these files control which instructions a harness reads and which actions it can take. Developers may also commit the specs, plans, and task files that agentic coding tools and spec-driven development frameworks write for agents to implement. The AgentTorrent dataset records these files for six proprietary and open-source agentic coding tools in 10,944 engineered software projects on GitHub. It contains 62,374 configuration artifacts, the files that these artifacts import, 27,593 agentic planning documents, and more than 1.2 million commits that credit an AI tool. All heuristics used for data collection are grounded in tool documentation or derived from GitHub repositories, and we evaluate their detection accuracy with each collection. We use open-weight models to classify whether GitHub repositories host engineered software projects. Unlike most datasets, AgentTorrent links configuration artifacts, imported files, agentic planning documents, and commits that credit an AI tool within the same repositories. Configuration artifacts, planning documents, and credited commits occur together in 388 repositories. For example, our dataset allows researchers to study whether a tool is credited in commits that add instructions for that same tool and how instruction files import and contradict each other. Our data collection pipeline supports regular updates and automatically checks vendor documentation and reports unknown artifacts and agentic coding tools. The dataset and the pipeline are open-source and are archived on Zenodo.

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